U.S. patent application number 13/435845 was filed with the patent office on 2012-10-11 for seismic image enhancement.
Invention is credited to Saul Antonio Trujillo Muhl.
Application Number | 20120257476 13/435845 |
Document ID | / |
Family ID | 46966035 |
Filed Date | 2012-10-11 |
United States Patent
Application |
20120257476 |
Kind Code |
A1 |
Muhl; Saul Antonio
Trujillo |
October 11, 2012 |
SEISMIC IMAGE ENHANCEMENT
Abstract
A method can include accessing seismic data; providing a wave
function that defines, at least in part, a correlation window
length; generating local autocorrelation functions for the seismic
data using the correlation window length; performing
cross-correlations between the wave function and each of the local
autocorrelation functions to provide local cross-correlation
coefficient values; determining second derivatives of the local
cross-correlation coefficient values to provide local second
derivative values; and rendering the local second derivative values
to a display. Various other apparatuses, systems, methods, etc.,
are also disclosed.
Inventors: |
Muhl; Saul Antonio Trujillo;
(Bogota, CO) |
Family ID: |
46966035 |
Appl. No.: |
13/435845 |
Filed: |
March 30, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61472084 |
Apr 5, 2011 |
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Current U.S.
Class: |
367/38 |
Current CPC
Class: |
G01V 1/307 20130101;
G01V 1/366 20130101; G01V 1/325 20130101 |
Class at
Publication: |
367/38 |
International
Class: |
G01V 1/34 20060101
G01V001/34; G01V 1/28 20060101 G01V001/28 |
Claims
1. A method comprising: accessing seismic data; providing a wave
function that defines, at least in part, a correlation window
length; generating local autocorrelation functions for the seismic
data using the correlation window length; performing
cross-correlations between the wave function and each of the local
autocorrelation functions to provide local cross-correlation
coefficient values; determining second derivatives of the local
cross-correlation coefficient values to provide local second
derivative values; and rendering the local second derivative values
to a display.
2. The method of claim 1 wherein the accessing seismic data
comprises accessing seismic data as amplitude versus time or depth
and a spatial dimension.
3. The method of claim 1 wherein the rendering the local second
derivative values to a display comprises rendering the local second
derivative values versus time or depth and a spatial dimension.
4. The method of claim 1 further comprising picking one or more
horizons based on the rendering of the local second derivative
values to the display.
5. The method of claim 1 wherein the providing a wave function
comprises providing a cosine function for a single frequency.
6. The method of claim 5 further comprising repeating the method
wherein the providing a cosine function for a single frequency
comprises, for each repetition of the method, providing a cosine
function for a different single frequency.
7. The method of claim 1 further comprising rendering a graphical
user interface to the display wherein the graphical user interface
comprises a graphical control for input of a frequency for the wave
function.
8. The method of claim 1 further comprising rendering a graphical
user interface to the display wherein the graphical user interface
comprises a graphical control for selection of an attribute that
effectuates at least the performing cross-correlations.
9. The method of claim 1 further comprising rendering a graphical
user interface to the display wherein the graphical user interface
comprises a graphical control for selection of an attribute that
effectuates at least the performing cross-correlations and the
determining second derivatives.
10. One or more computer-readable media comprising
computer-executable instructions to instruct a computing system to:
access seismic data from a storage device; receive at least one
parameter to define a wave function that determines, at least in
part, a correlation window length; generate local autocorrelation
functions for the seismic data using the correlation window length;
perform cross-correlations between the wave function and each of
the local autocorrelation functions to provide local
cross-correlation coefficient values; determine second derivatives
of the local cross-correlation coefficient values to provide local
second derivative values; and store the local second derivative
values to a storage device.
11. The one or more computer-readable media of claim 10 further
comprising computer-executable instructions to instruct a computer
system to render a graphical user interface to a display for
display of a selectable attribute to instruct the computer system
to execute the instructions to perform cross-correlations and to
execute the instructions to determine second derivatives.
12. The one or more computer-readable media of claim 10 wherein the
computer-executable instructions to instruct a computer system to
receive at least one parameter comprises instructions to receive a
frequency for the wave function.
13. The one or more computer-readable media of claim 10 further
comprising computer-executable instructions to instruct a computer
system to render the local second derivative values to a
display.
14. The one or more computer-readable media of claim 13 wherein the
computer-executable instructions to instruct a computer system to
render the local second derivative values to a display comprise
computer-executable instructions to render the second derivative
values to the display using a color scheme.
15. A system comprising: one or more processors; memory; a network
interface; a display interface; and processor-executable
instructions stored in the memory to receive seismic data via the
network interface, generate local autocorrelation functions for the
seismic data using a correlation window length, perform
cross-correlations between a wave function and each of the local
autocorrelation functions to provide local cross-correlation
coefficient values, determine second derivatives of the local
cross-correlation coefficient values to provide local second
derivative values, and transmit signals via the display interface
to render the local second derivative values to a display.
16. The system of claim 15 wherein the wave function comprises a
cosine function.
17. The system of claim 15 wherein the wave function comprises a
wave function characterized by a single frequency.
18. The system of claim 15 wherein the seismic data comprises
seismic data as amplitude versus time or depth and a spatial
dimension.
19. The system of claim 15 wherein the signals to render the local
second derivative values to a display comprises signals to render
the local second derivative values versus time or depth and a
spatial dimension.
20. The system of claim 15 further comprising processor-executable
instructions stored in the memory to pick a horizon responsive to
receipt of an input command during rendering of the local second
derivative values to a display.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application having Ser. No. 61/472,084 entitled "Method, System,
Apparatus and Computer Readable Medium for Seismic Image
Enhancement," filed Apr. 5, 2011, which is incorporated by
reference herein.
BACKGROUND
[0002] Seismic interpretation is a process that may examine seismic
data (e.g., location and time or depth) in an effort to identify
subsurface structures such as horizons and faults. Structures may
be, for example, faulted stratigraphic formations indicative of
hydrocarbon traps or flow channels. In the field of resource
extraction, enhancements to seismic interpretation can allow for
construction of a more accurate model, which, in turn, may improve
seismic volume analysis for purposes of resource extraction.
Various techniques described herein pertain to processing of
seismic data, for example, for analysis of such data (e.g., for
identifying structures in a geologic environment).
SUMMARY
[0003] A method can include generating local autocorrelation
functions for seismic data and performing cross-correlations
between each of the local autocorrelation functions with a wave
function to provide cross-correlation coefficient values where
second derivative values are determined for the cross-correlation
coefficient values. The resulting second derivative values may be
rendered to a display for purposes of analysis.
[0004] One or more computer-readable media may include
computer-executable instructions to generate local autocorrelation
functions for seismic data, perform cross-correlations between the
wave function and each of the local autocorrelation functions to
provide local cross-correlation coefficient values and to determine
second derivatives of the local cross-correlation coefficient
values to provide local second derivative values.
[0005] A system may include one or more processors; memory; a
network interface; a display interface; and processor-executable
instructions stored in the memory to receive seismic data via the
network interface, generate local autocorrelation functions for the
seismic data (e.g., using a correlation window length), perform
cross-correlations between a wave function and each of the local
autocorrelation functions to provide local cross-correlation
coefficient values, determine second derivatives of the local
cross-correlation coefficient values to provide local second
derivative values, and transmit signals via the display interface
to render the local second derivative values to a display. Various
other apparatuses, systems, methods, etc., are also disclosed.
[0006] This summary is provided to introduce a selection of
concepts that are further described below in the detailed
description. This summary is not intended to identify key or
essential features of the claimed subject matter, nor is it
intended to be used as an aid in limiting the scope of the claimed
subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0008] Features and advantages of the described implementations can
be more readily understood by reference to the following
description taken in conjunction with the accompanying
drawings.
[0009] FIGS. 1.1 to 1.4 illustrate simplified, schematic views of
an example of an oilfield;
[0010] FIG. 2 illustrates a schematic view, partially in cross
section of an example of an oilfield;
[0011] FIG. 3 illustrates a schematic view of an example of a
production system for performing one or more oilfield
operations;
[0012] FIG. 4 illustrates an example of a method;
[0013] FIG. 5 illustrates an example of a transform process that
provides for cross-correlation coefficient values;
[0014] FIG. 6 illustrates an example of a graphical user
interface;
[0015] FIG. 7 illustrates examples of discretized derivatives;
[0016] FIG. 8 illustrates examples of cross-correlation coefficient
values for various different frequencies of a cosine function;
[0017] FIG. 9 illustrates, in color, an example of seismic
data;
[0018] FIG. 10 illustrates, in color, an example of
cross-correlation coefficient values;
[0019] FIG. 11 illustrates, in color, an example of second
derivative values of the cross-correlation coefficient values of
FIG. 10;
[0020] FIG. 12 illustrates an example of a system; and
[0021] FIG. 13 illustrates an example of system components and an
example of a network system.
DETAILED DESCRIPTION
[0022] The following description includes the best mode presently
contemplated for practicing the described implementations. This
description is not to be taken in a limiting sense, but rather is
made merely for the purpose of describing the general principles of
the implementations. The scope of the described implementations
should be ascertained with reference to the issued claims.
[0023] As an example, seismic image enhance (SIE) may be performed
using seismic to simulation software. For example, the PETREL.RTM.
seismic to simulation software framework (Schlumberger Limited,
Houston, Tex.) includes various features to perform SIE (e.g., with
respect to a 3D seismic cube, a 2D seismic line, etc.). As an
example, it may not be necessary to re-process seismic data prior
to performing SIE. For example, it may only be necessary to know
the dominant frequency of seismic data which will be duplicated and
used with an iso-frequency component attribute (e.g., as an input
parameter).
[0024] In the PETREL.RTM. framework, a user may apply the
iso-frequency component attribute to seismic data and the
parameters to obtain a value of a dominant frequency doubled in the
seismic data. Such a process may provide processed seismic data in
a "frequency domain" where, to return the processed seismic data to
an "amplitude domain", a second derivative attribute may be
applied. As an example, application of an iso-frequency component
attribute followed by application of a second derivative attribute
may provide a user with more data frequency content, which may, in
turn, allow a user to identify more stratigraphic and structural
features represented by the seismic data.
[0025] As an example, consider 2D seismic data provided as
amplitude versus time/depth and position (see, e.g., FIG. 9). In
such an example, an iso-frequency component attribute may be
applied locally to the 2D seismic data using a selected frequency
and cycle length where the frequency and the cycle length determine
a "correlation window length" (e.g., in units of time) where the
"correlation window" is applied locally. As to the selected
frequency, the value may be selected, for example, depending on the
average frequency content of the seismic data under consideration.
The iso-frequency component attribute may perform autocorrelation
locally on the 2D seismic data (e.g., using the correlation window
length) to generate local autocorrelation functions with respect to
time/depth and position and then perform cross-correlation for the
selected frequency, for example, using a wave function (e.g., a
cosine function) applied locally to generate local values for
cross-correlation coefficients (see, e.g., FIG. 10). Given the
local values for cross-correlation coefficients, a second
derivative operation may be applied to provide values for the
second derivative of the cross-correlation values (e.g., time/depth
versus position) where the second derivative is taken with respect
to time/depth (see, e.g., FIG. 11). In such an example, the second
derivative of the cross-correlation values can yield an attribute
that can be thought of (e.g., and viewed) as an amplitude rather
than a frequency (e.g., or a frequency correlation value). Thus, as
an example, by taking the second derivative of the correlation
coefficient values, seismic data may be enhanced (e.g., whether 2D
or 3D seismic data).
[0026] As an example, a process may be applied to 3D seismic data,
optionally to provide a horizontal slice, a vertical slice or other
slice through the 3D seismic data where features are enhanced by
applying autocorrelation, cross-correlation and second derivative
operations (e.g., optionally successive first derivative
operations).
[0027] In various examples, a method can include applying a second
derivative operation to cross-correlation coefficient values, for
example, to improve frequency content of seismic data, which may
allow a seismic interpreter to visualize features otherwise
difficult to discern.
[0028] As an example, an iso-frequency component attribute may be
applied to seismic data. The iso-frequency component attribute may
represent a cross-correlation function of auto-correlation of
seismic data and a kernel function. As an example, a kernel
function may be a wave function such as a cosine function. Where a
cosine function is provided, the cross-correlation may be referred
to as the "correlation cosine transform" or "cosine-correlation
transform" (CCT) technique. The CCT technique may result in a
"frequency" value as a measure of a contribution of a frequency
(e.g., optionally defined by a user). An intermediate result of a
method that includes application of a cross-correlation technique
may include data showing a cross-correlation coefficient of
similarity between autocorrelations of seismic data and a kernel
function.
[0029] Another attribute, referred to as a "second derivative
attribute" may be applied to seismic data related to the
iso-frequency component attribute output. Upon application of a
second derivative operation, seismic data may return, effectively,
to the domain of amplitude. As a result, a seismic interpreter may
be able to identify certain stratigraphic and structural features
represented by the seismic data. For time-sampled seismic data, a
second derivative may be approximated using a discretized
approximate form.
[0030] As an example, a workflow may include input of seismic data
(see e.g., FIG. 9), applying an iso-frequency component operation
(see e.g., FIG. 10), and applying a second derivative operation for
output of a seismic enhanced image (SIE) (see e.g., FIG. 11).
[0031] As an example, a method for performing SIE may include
providing input, where the input includes a predetermined volume
containing one or more structural and stratigraphic features. The
input may further include a plurality of 2D or 3D seismic traces
that include a plurality of reflectivity features including
frequency content acquisition, without a predetermined specific
processing. In such an example, the plurality of seismic traces may
represent elastic characteristics of a geological feature. A method
may further include applying a spectral decomposition, thereby
producing a second plurality of seismic traces containing a
plurality of autocorrelation functions with a dynamic range, for
example, between 1 and -1. The method may also include applying a
differential equation to the second plurality of seismic traces,
thereby producing a third plurality of seismic traces that include
a second plurality of reflectivity features. The third plurality of
seismic traces may represent one or more stratigraphic and
structural features that may be interpreted using a seismic
interpreter.
[0032] As an example, a method can perform seismic image
enhancement (SIE) using a first pass that converts seismic data in
an amplitude domain to a frequency domain (e.g., using an
iso-frequency component operation). Such a first pass may be a
"seismic decomposition" operation (see, e.g., U.S. Pat. No.
6,757,614, which is incorporated by reference herein). A second
pass operation, for example, related to a 3D seismic cube or a 2D
seismic line in the frequency domain may be applied in the form of
a second derivative operation. Such a second pass operation may
convert information in the frequency domain, effectively, to the
domain of amplitude (e.g., with higher frequency content).
[0033] FIGS. 1.1 through 1.4 illustrate simplified, schematic views
of an example of an oilfield 100 that includes a subterranean
formation 102 with a reservoir 104 therein in accordance with
implementations of various examples of technologies and examples of
techniques described herein. FIG. 1.1 illustrates an example of a
survey operation being performed by a survey tool, such as a
seismic truck 106.1, to measure properties of the subterranean
formation where, for example, the survey operation is a seismic
survey operation for producing sound vibrations. In FIG. 1.1, one
such sound vibration, a sound vibration 112 generated by a source
110, reflects off horizons 114 in an earth formation 116. A set of
sound vibrations is received by sensors, such as geophone-receivers
118, situated on the earth's surface. The data received 120 is
provided as input data to, for example, a computer 122.1 of a
seismic truck 106.1, and responsive to the input data, the computer
122.1 generates seismic data output 124. This seismic data output
may be stored, transmitted or further processed as desired, for
example, by data reduction.
[0034] FIG. 1.2 illustrates an example of a drilling operation
being performed by drilling tools 106.2 suspended by a rig 128 and
advanced into the subterranean formation 102 to form a wellbore
136. In the example of FIG. 1.2, a mud pit 130 is used to draw
drilling mud into the drilling tools via a flow line 132 for
circulating drilling mud down through the drilling tools, then up
the wellbore 136 and back to the surface. The drilling tools are
advanced into the subterranean formation 102 to reach the reservoir
104. Each well may target one or more reservoirs. The drilling
tools may be adapted for measuring downhole properties (e.g., using
logging while drilling). Such logging while drilling tools may also
be adapted for taking a core sample 133 as shown.
[0035] Computer facilities may be positioned at various locations
about the oilfield 100 (see, e.g., the surface unit 134) and/or at
remote locations. The surface unit 134 may be used to communicate
with the drilling tools and/or offsite operations, as well as with
other surface or downhole sensors. The surface unit 134 may be
capable of communicating with the drilling tools to send commands
to the drilling tools, and to receive data therefrom. The surface
unit 134 may also collect data generated during the drilling
operation and produce data output 135, which may then be stored,
transmitted, etc.
[0036] Sensors (S), such as gauges, may be positioned about the
oilfield 100 to collect data relating to various oilfield
operations as described previously. As shown in the example of FIG.
1.2, a sensor (S) may be positioned in one or more locations in the
drilling tools and/or at the rig 128 to measure drilling
parameters, such as weight on bit, torque on bit, pressures,
temperatures, flow rates, compositions, rotary speed, and/or other
parameters of the field operation. Sensors (S) may also be
positioned in one or more locations in the circulating system.
[0037] The drilling tools 106.2 may include a bottom hole assembly
(BHA) (not shown), generally referenced, near the drill bit (e.g.,
within several drill collar lengths from the drill bit). The bottom
hole assembly can include capabilities for measuring, processing,
and storing information, as well as communicating with the surface
unit 134. The bottom hole assembly further includes drill collars
for performing various other measurement functions.
[0038] The bottom hole assembly may include a communication
subassembly that communicates with the surface unit 134. The
communication subassembly is adapted to send signals to and receive
signals from the surface using a communications channel such as mud
pulse telemetry, electro-magnetic telemetry, or wired drill pipe
communications. The communication subassembly may include, for
example, a transmitter that generates a signal, such as an acoustic
or electromagnetic signal, which is representative of the measured
drilling parameters. It will be appreciated by one of skill in the
art that a variety of telemetry systems may be employed, such as
wired drill pipe, electromagnetic or other known telemetry
systems.
[0039] Typically, the wellbore is drilled according to a drilling
plan that is established prior to drilling. The drilling plan
typically sets forth equipment, pressures, trajectories and/or
other parameters that define the drilling process for the wellsite.
The drilling operation may then be performed according to the
drilling plan. However, as information is gathered, the drilling
operation may need to deviate from the drilling plan. Additionally,
as drilling or other operations are performed; the subsurface
conditions may change. The earth model may also need adjustment as
new information is collected.
[0040] The data gathered by sensors (S) may be collected by the
surface unit 134 and/or other data collection sources for analysis
or other processing. The data collected by sensors (S) may be used
alone or in combination with other data. The data may be collected
in one or more databases and/or transmitted on or offsite. The data
may be historical data, real time data, or combinations thereof.
The real time data may be used in real time, or stored for later
use. The data may also be combined with historical data or other
inputs for further analysis. The data may be stored in separate
databases, or combined into a single database.
[0041] The surface unit 134 may include a transceiver 137 to allow
communications between the surface unit 134 and various portions of
the oilfield 100 or other locations. The surface unit 134 may also
be provided with or functionally connected to one or more
controllers (not shown) for actuating mechanisms at the oilfield
100. The surface unit 134 may then send command signals to the
oilfield 100 in response to data received. The surface unit 134 may
receive commands via the transceiver 137 or may itself execute
commands to the controller. A processor may be provided to analyze
the data (locally or remotely), make the decisions and/or actuate
the controller. In this manner, the oilfield 100 may be selectively
adjusted based on the data collected. Such a technique may be used
to optimize portions of the field operation, such as controlling
drilling, weight on bit, pump rates, or other parameters. Such
adjustments may be made, for example, automatically based on
computer protocol, and/or manually by an operator. In some cases,
well plans may be adjusted to select optimum operating conditions,
or to avoid problems.
[0042] FIG. 1.3 illustrates an example of a wireline operation
being performed by a wireline tool 106.3 suspended by the rig 128
and into the wellbore 136. The wireline tool 106.3 may be adapted
for deployment into the wellbore 136 for generating well logs,
performing downhole tests and/or collecting samples. The wireline
tool 106.3 may optionally help to perform one or more other
operations, for example, placement of an explosive, radioactive,
electrical, or acoustic energy source 144 that sends and/or
receives electrical signals to surrounding the subterranean
formation 102 (e.g., and fluids therein).
[0043] The wireline tool 106.3 may be operatively connected to, for
example, the geophones 118 and the computer 122.1 of the seismic
truck 106.1. The wireline tool 106.3 may also provide data to the
surface unit 134. The surface unit 134 may collect data generated
during the wireline operation and may produce the data output 135
that may be stored, transmitted, etc. The wireline tool 106.3 may
be positioned at various depths in the wellbore 136 to provide a
survey or other information relating to the subterranean formation
102.
[0044] Sensors (S), such as gauges, may be positioned about the
oilfield 100 to collect data relating to various field operations
as described previously. As shown, sensor S is positioned in the
wireline tool 106.3 to measure downhole parameters which relate to,
for example porosity, permeability, fluid composition and/or other
parameters of the field operation.
[0045] FIG. 1.4 illustrates an example of a production operation
being performed by a production tool 106.4 deployed from a
production unit or a Christmas tree 129 and into a completed
wellbore 137 for drawing fluid from the downhole reservoirs into
surface facilities 142. The fluid flows from the reservoir 104
through perforations in the casing (not shown) and into the
production tool 106.4 in the completed wellbore 137 and to the
surface facilities 142 via a gathering network 146.
[0046] Sensors (S), such as gauges, may be positioned about the
oilfield 100 to collect data relating to various field operations
as described previously. As shown, the sensor (S) may be positioned
in the production tool 106.4 or associated equipment, such as the
Christmas tree 129, the gathering network 146, the surface facility
142, and/or the production facility, to measure fluid parameters,
such as fluid composition, flow rates, pressures, temperatures,
and/or other parameters of the production operation.
[0047] Production may also include injection wells for added
recovery. One or more gathering facilities may be operatively
connected to one or more of the wellsites for selectively
collecting downhole fluids from the wellsite(s).
[0048] While the examples of FIGS. 1.2 through 1.4 illustrate some
tools used to measure properties of an oilfield, it will be
appreciated that the tools may be used in connection with
non-oilfield operations, such as gas fields, mines, aquifers,
storage, or other subterranean facilities. Also, while certain data
acquisition tools are depicted, it will be appreciated that various
measurement tools capable of sensing parameters, such as seismic
two-way travel time, density, resistivity, production rate, etc.,
of the subterranean formation and/or its geological formations may
be used. Various sensors (S) may be located at various positions
along the wellbore and/or the monitoring tools to collect and/or
monitor the desired data. Other sources of data may also be
provided from offsite locations.
[0049] The field configurations of the examples of FIGS. 1.1
through 1.4 are intended to provide a brief description of an
example of a field usable with oilfield application frameworks.
Part, or all, of the oilfield 100 may be on land, water, and/or
sea. Also, while a single field measured at a single location is
depicted, oilfield applications may be utilized with any
combination of one or more oilfields, one or more processing
facilities and one or more wellsites.
[0050] FIG. 2 shows a schematic view, partially in cross section of
an example of an oilfield 200 having data acquisition tools 202.1,
202.2, 202.3 and 202.4 positioned at various locations along the
oilfield 200 for collecting data of a subterranean formation 204.
The data acquisition tools 202.1, 202.2, 203.3 and 202.4 may be,
for example, provided as the data acquisition tools 106.1, 106.2,
106.3 and 106.4 of the examples of FIGS. 1.1 through 1.4,
respectively, or others not depicted. As shown in the example of
FIG. 2, the data acquisition tools 202.1, 202.2, 202.3 and 202.4
can generate data plots or measurements 208.1, 208.2, 208.3, 208.4,
202.1, 202.2, 202.3 and 202.4, respectively. Such data plots are
depicted along the oilfield 200 as examples to demonstrate data
generated by various operations.
[0051] In FIG. 2, the data plots 208.1, 208.2, and 208.3 are
examples of static data plots that may be generated by the data
acquisition tools 202.1, 202.2, 202.3 and 202.4, respectively. The
static data plot 208.1 is an example of a seismic two-way response
time (TWT). The static plot 208.2 is an example of a core sample
data measured from a core sample of the formation 204. The static
data plot 208.3 is an example of a logging trace. A production
decline curve or graph 208.4 is an example of a dynamic data plot
of the fluid flow rate over time. Other data may also be collected,
such as historical data, user inputs, economic information and/or
other measurement data and other parameters of interest.
[0052] In FIG. 2, the subterranean structure 204 has a plurality of
geological formations or layers. As shown, the several formations
or layers include a shale layer 206.1, a carbonate layer 206.2, a
shale layer 206.3, and a sand layer 206.4. A fault 207 extends
through the layers 206.1 and 206.2. The static data acquisition
tools may be adapted to take measurements and detect
characteristics of the formations.
[0053] While a specific subterranean formation with specific
geological structures is depicted in FIG. 2, it will be appreciated
that the oilfield 200 may contain a variety of geological
structures and/or formations. In some locations, typically below
the water line, fluid may occupy pore spaces of the formations.
Each of the measurement devices may be used to measure properties
of the formations and/or its geological features. While each
acquisition tool is shown as being in specific locations in the
oilfield 200, it will be appreciated that one or more types of
measurement may be taken at one or more location across one or more
oilfields or other locations for comparison and/or analysis.
[0054] Data collected from various sources, such as the data
acquisition tools of FIG. 2, may be processed and/or evaluated. As
an example, seismic data displayed in the static data plot 208.1
from the data acquisition tool 202.1 may be analyzed by a
geophysicist to determine characteristics of the subterranean
formations and features. As an example, core data shown in the
static plot 208.2 and/or log data from the well log 208.3 may be
analyzed by a geologist to determine various characteristics of the
subterranean formation. As an example, production data from the
graph 208.4 may be analyzed by a reservoir engineer to determine
fluid flow reservoir characteristics. Data analyzed by a geologist,
geophysicist, a reservoir engineer, etc., may optionally be
analyzed using one or more modeling techniques.
[0055] FIG. 3 shows an example of an oilfield 300 for performing
production operations. As shown, the oilfield 300 includes a
plurality of wellsites 302 operatively connected to a processing
facility 354. Part, or all, of the oilfield 300 may be on land
and/or sea. Also, while a single oilfield with a single processing
facility and a plurality of wellsites is depicted any combination
of one or more oilfields, one or more processing facilities and one
or more wellsites may be present.
[0056] In the example of FIG. 3, each of the one or more wellsites
302 includes equipment that forms a respective wellbore 336 into
the earth. Each of the wellbores 336 extends through a subterranean
formation 306, which includes various layers and reservoirs 304.
Such reservoirs may contain fluids, such as hydrocarbons (e.g., in
one or more phases). In the example of FIG. 3, each of the
wellsites 302 may draw fluid from one or more of the reservoirs 304
and pass such fluid or fluids to the processing facility 354, for
example, via one or more surface networks 344. As an example, each
of the surface networks 344 can include tubing and control
mechanisms for controlling the flow of fluids from one or more
wellsites 302 to the processing facility 354.
[0057] Given the various examples of FIGS. 1.1, 1.2, 1.3, 1.4, 2
and 3, various aspects of seismic data (e.g., reflection data) are
described along with processing of such data, for example, to
enhance data for identification of one or more features.
[0058] Seismic reflection data includes information about
subsurface geology, physical rock properties, etc. Features may be
inferred from reflected wave travel-time between source and arrival
at one or more receivers. As an example, a two-way travel-time
(TWT) may be defined by the time taken for a seismic wave to travel
from a source to a boundary (e.g., between layers with a different
seismic velocity, density, and acoustic impedance) where reflected
energy returns to a receiver. A contrast between acoustic impedance
may be referred to as a reflection coefficient, which may represent
an interface (e.g., a boundary). Arrival of reflected seismic waves
can produce systematic variations from trace to trace. Such
variations may be referred to as seismic events, possibly
interpreted as real geological interfaces between layers with
different reflection coefficients. Measuring travel-time of seismic
events can allow for determination of attitude and location of the
geological interfaces. An interpretation process may take into
account amplitude, frequency, phase, wave shape variations,
etc.
[0059] Three-dimensional seismic data acquisition can provide a
cube of seismic data relative to a three-dimensional coordinate
system, for example, X, Y and depth Z, which may be time.
Three-dimensional seismic data may be organized in inlines (e.g.,
according to an acquisition direction) and crosslines (e.g., in a
direction perpendicular to an acquisition direction).
Three-dimensional seismic data may allow for mapping horizons and
following of seismic events along at least a portion of an
acquisition survey area. Such interpretations may facilitate
building of a geological model (e.g., optionally a reservoir
model).
[0060] In three-dimensional seismic data, an individual seismic
trace may be considered to be a seismic wavelet convolution
resulting from travel of seismic energy emitted by a source through
a subsurface where the seismic wavelet convolution includes
reflection coefficients (e.g., in series) derived from properties
of the subsurface (e.g., density and seismic velocity of different
rock layers crossed by seismic energy emitted by the source).
[0061] Various types of processing may be applied to seismic data,
for example, consider correction, filtering, deconvolution, etc. A
deconvolution process may aim to compress wavelet shape, recover
high-frequencies, attenuate reverberations and short-period
multiples, for example, to increase vertical (depth) resolution of
reflectors and to normalize the frequency spectrum of the seismic
data being processed. A deconvolution process may uncover one or
more reflection coefficients, for example, to form a series of
reflection coefficients. As to corrections, as an example, a Normal
Moveout (NMO) Correction may be applied (e.g., to remove effects of
source-receiver offset and even out TWT). After some processing,
seismic traces may be "stacked" (e.g., by positioning seismic
reflections to their "true" subsurface depth or depths). Various
processing techniques may be applied to seismic data pre-stack or
post-stack.
[0062] After one or more processing techniques have been applied to
seismic data, interpretations may be made using processed seismic
data; noting that an interpretation process may include applying
one or more additional processing techniques. As an example, an
additional processing technique may aim to enhance processed
seismic data and be referred to as a seismic image enhancement
(SIE) technique. An SIE technique may facilitate recognition of one
or more seismic patterns (e.g., features) germane to potential
hydrocarbon accumulations sites, depositional environments,
structural geology, etc.
[0063] An interpretation process may involve visual display of
seismic data and interaction using one or more tools (e.g.,
executable instruction modules stored in memory and executed by one
or more processors). An interpretation process may consider
vertical seismic sections, inline and crossline directions,
horizontal seismic sections called horizontal time slices, etc.
Seismic data may optionally be interpreted with other data such as
well log data. An interpretation process may include associating
seismic reflectors to boundaries of known lithological layers.
Features such as faults and seismic reflectors (e.g., horizons) may
be interpreted, for example, in a travel-time domain, in an
amplitude domain (e.g., as to amplitude content). An interpretation
process may include identifying, reducing, etc., a number of
mis-ties (e.g., mismatches between seismic data and well log data,
crossing of seismic lines and mismatch of seismic reflectors,
etc.).
[0064] An interpretation process may include loading seismic data
(e.g., from a data store optionally via a network connection).
Seismic data may be formatted according to the SEG-Y format
standard (Society of Exploration Geophysicists), the ZGY format
standard (e.g., a bricked format) or another format. Seismic data
may optionally be loaded, for example, according to a number of
traces.
[0065] An interpretation process may include determining one or
more seismic attributes. A seismic attribute may be considered, for
example, a way to describe, quantify, etc., characteristic content
of seismic data. As an example, a quantified characteristic may be
computed, measured, etc., from seismic data. A seismic attribute
may be a rate of change of a quantity (or quantities) with respect
to time, space or both time and space. As an example, a seismic
attribute may provide for examination of seismic data in an
amplitude domain, in a time domain, or in another manner.
[0066] An interpretation framework may include modules to determine
one or more seismic attributes. Seismic attributes may optionally
be classified, for example, as volume attributes or surface
attributes. As an example, a volume attribute may be an attribute
computed from a seismic cube and may result in a new seismic cube
that includes information pertaining to the volume attribute. As an
example, a surface attribute may be a value associated with a
surface of a seismic cube that includes information pertaining to a
volume attribute.
[0067] A seismic attributes may be derived from seismic wavelet
components. As an example, amplitude content in seismic data may
provide for determining one or more physical characteristics about
a subsurface (e.g., acoustic impedance, reflection coefficients,
velocities, absorption effects). As an example, phase content in
seismic data may provide for determination of shape and geometry of
reflectors (e.g., for interpretation of seismic stratigraphy and
depositional regimes). As an example, frequency content in seismic
data may provide for determination of stratigraphic events, fault
interpretation due to absorption effects, forecast of reservoir
properties, interpretation of additional geologic layering (e.g.,
combine with amplitude content), etc.
[0068] As a seismic interpretation may be performed using
displayable information. For example, information may be rendered
to a display device, a projection device, a printing device, etc.
As an example, one or more color schemes may be referenced for
displayable information, for example, to enhance visual examination
of displayable information. As an example, a color scheme may
include a palette, a range, etc. A look-up-table (LUT) or other
data structure, function (e.g., linear or non-linear), etc., may
allow for mapping of values associated with one or more seismic
attributes to colors (e.g., RGB, YCbCr, etc.). Where the human eye
will be used or is used for viewing displayable information, a
color scheme may be selected to enhance interpretation (e.g.,
distinguishing features of displayable information).
[0069] A module for determining one or more seismic attributes may
include one or more parameters. As an example, a module may include
one or more parameters that may be set via a graphic user
interface, a specification file, etc. In such an example, an
interpreter may wish to examine a seismic attribute for seismic
data using one or more values of a parameter. As an example, such a
module may provide a default value and a field, graphical control,
etc., that allows for input of a value.
[0070] One or more seismic attributes may pertain to seismic signal
processing of seismic data. Such processing may act on frequency,
amplitude or other aspects of seismic data. As an example, seismic
signal processing may operate on a seismic trace, which may be
provided in an amplitude domain. In an amplitude domain, a seismic
trace may be represented as a function with respect to time (e.g.,
f(t)). For example, in a two-dimensional plot, the abscissa may
correspond to time and the ordinate may correspond to
amplitude.
[0071] For a seismic trace in an amplitude domain represented by
f(t), a first derivative with respect to time may be represented by
df(t)/dt or in a discretized form for a digitized seismic trace
by:
f ( t ) t = [ f ( t - 2 ) - f ( t + 2 ) ] 12 - 8 [ f ( t - 1 ) - f
( t + 1 ) ] 12 ##EQU00001##
[0072] A first derivative seismic attribute may include the
foregoing equation (e.g., or other approximation equation) and be
applied to one or more seismic traces. Such an attribute may be
considered as phase shifting by 90.degree. the one or more seismic
traces. Such an attribute may provide information germane to
quality of signal consistency (e.g., positive or negative peaks may
produce zero crossings) and improve correlation between seismic
data and lithology-indicative well log data.
[0073] Where acquired seismic data has been processed to provide
approximately zero-phase seismic data, a comparison may be made
between such data and first derivative seismic attribute
information (e.g., 90.degree. phase shifted). A visual comparison
may provide for observation of increased vertical resolution and
sharpness of seismic reflectors, which, for example, may facilitate
thin-bed interpretation. A first derivate seismic attribute volumes
(e.g., calculated from zero-phase seismic data), may enhance
interpretability and, for example, may be used as seismic
conditioning for a picking tool for seismic reflector
interpretation.
[0074] For a seismic trace in an amplitude domain represented by
f(t), a second derivative with respect to time may be represented
by d.sup.2f(t)/dt.sup.2 or in a discretized form for a digitized
seismic trace by:
2 f ( t ) t 2 = f ( t - 1 ) - f ( t + 1 ) - 2 f ( t )
##EQU00002##
[0075] As an example, a second derivative seismic attribute may be
implemented in one or more manners. For example, consider applying
a first derivative seismic attribute twice to provide two
90.degree. phase shifts for a total phase shift of 180.degree.. A
phase shift of 180.degree. can provide a second derivative seismic
attribute that includes traces of inverted polarity such that peaks
become troughs and troughs become peaks.
[0076] As to frequency, a dominant frequency seismic attribute may
be provided. As an example, a dominant frequency seismic attribute
may leverage one or more other seismic attributes. For example,
where an instantaneous frequency seismic attribute and an
instantaneous bandwidth attribute are provided, a dominant
frequency seismic attribute may sum a square of instantaneous
frequency with the square of instantaneous bandwidth and determine
a square root of the sum to represent a root mean square (RMS)
frequency of the amplitude spectrum.
[0077] A seismic attribute may pertain to stratigraphic features in
seismic data and, for example, facilitate identification of
stratigraphic sequences, lateral and vertical variations of
lithologies, structural orientation measurements, frequency
decomposition, facies distribution, etc.
[0078] As an example, consider an iso-frequency seismic attribute.
Such an attribute may include use of an iso-frequency component to
generate a volume attribute through a seismic decomposition
technique.
[0079] As to a seismic decomposition technique, consider as an
example a spectral decomposition performed locally that includes
generating an autocorrelation function of seismic data on a time
window. In such an example, the autocorrelation function tends to
be insensitive to phase content of the seismic data, thus aligning
the seismic energy at zero lag. A subsequent process can include
performing a cross-correlation between a wave function such as a
cosine wave function (correlation-cosine transform "CCT", e.g.,
with a defined number of cycles) and the generated autocorrelation
function where the cross-correlation determines numeric similarity
of the autocorrelation function and the wave function.
[0080] As an example, a cross-correlation algorithm may include an
equation such as:
.phi. GH ( .tau. ) = k = - N N G ( k ) H ( k + .tau. ) [ k = - N N
[ G 2 ( k ) k = - N N H 2 ( k ) ] ] 0.5 ##EQU00003##
[0081] In the above equation, G(k) and H(k) are signals being
correlated, for example, they may be windowed seismic data to
generate an autocorrelation function or a cosine function and a
generated autocorrelation function.
[0082] Output of a CCT technique may provide a correlation
coefficient that measures the correlation between a known cosine
wave signature of a specific frequency and the autocorrelation
seismic data. An iso-frequency seismic attribute three-dimensional
cube may be scaled, for example, between -1 to 1 where 0 indicates
an uncorrelated function, where 1 indicates identical signals and
where -1 indicates identical signals but inverted.
[0083] As to parameters, a cosine frequency parameter and a number
of cycles parameter may be provided. Such parameters may define a
correlation window length for extraction of the iso-frequency
seismic attribute. For example, a correlation window length may
depend on a relative frequency of a cosine function and a frequency
content of seismic data. While a cosine function is mentioned
(e.g., or cosine wave function), one or more other types of
functions may be used, for example, one or more other types of wave
functions. In a cross-correlation process, a function may be a
kernel function.
[0084] As to windowed seismic data, a short window tends to avoid
focusing of correlation energy, for example, to facilitate
identification of anomalies. As to a long window, it may facilitate
identification of local geologic effects (e.g., and not tuning
effects).
[0085] As an example, a module may provide an option to perform
spectral normalization, for example, a spectral whitening that may
act to remove a signature of a seismic wavelet (e.g., as associated
with a seismic energy source).
[0086] As an example, an iso-frequency seismic attribute may be
applied to seismic data, for example, to reveal subtle variations
in lithology (e.g., which may indicate stratigraphic traps for
hydrocarbons, etc.).
[0087] FIG. 4 shows an example of a method 400 that can enhance a
seismic image, for example, to facilitate analysis, identification
of features, etc. In the example of FIG. 4, the method 400 includes
an access block 410 for accessing seismic data, a provision block
420 for providing a wave function (e.g., a cosine function)
specified in terms of a frequency and a cycle length to a determine
a correlation window length (e.g., in units of time), a generation
block 430 for generating autocorrelation functions locally for the
seismic data (e.g., based in part on the correlation window
length), a performance block 440 for performing cross-correlation
of the autocorrelation functions and the wave function (e.g., for a
cosine function, a correlation-cosine transform) to provide
cross-correlation coefficient values of the functions, a
determination block 450 for determining second derivative values of
the cross-correlation coefficient values (e.g., with respect to
time or depth), and a render block 460 for rendering the second
derivatives values.
[0088] The method 400 is shown in FIG. 4 in association with
various computer-readable media (CRM) blocks 411, 421, 431, 441,
451 and 461. Such blocks generally include instructions suitable
for execution by one or more processors (or cores) to instruct a
computing device or system to perform one or more actions. Thus,
such instructions may be referred to as executable instructions
(e.g., computer-executable, processor-executable, etc.). While
various blocks are shown, a single medium may be configured with
instructions to allow for, at least in part, performance of various
actions of the method 400.
[0089] In the example of FIG. 4, the method 400 may access seismic
data (e.g., 2D, 3D, etc.) in an amplitude domain, for example,
renderable as amplitude in terms of intensity, color, etc., for
time or depth and position (e.g., along a line orthogonal to the
time or depth dimension). In such an example, a selected frequency
and cycle length for a wave function, such as a cosine function,
may determine a correlation window length, for example, in seconds
or depth units. The correlation window length may be applied
locally to the seismic data for purposes of generating local
autocorrelation functions. Given the local autocorrelation
functions, a cross-correlation technique may be applied locally
using the wave function to provide output in the form of values of
cross-correlation coefficients with respect to time or depth and
position. Such values may range, for example, from about -1 to
about +1, depending on how the local autocorrelation functions
cross-correlate with the wave function. Given such a matrix, an
approximation for a second derivative may be applied, for example,
along the time or depth dimension (e.g., a second derivative of
values of cross-correlation coefficients with respect to time or
depth). The resulting second derivatives may be provided in a
matrix form, for example, as second derivative values versus time
or depth and position. Such information may be rendered to a
display and appear in a manner akin to amplitude domain
information. In the foregoing example, by taking the second
derivative, cross-correlation information, which may be considered
as being in a "frequency domain", is effectively transformed to
information akin to that of an "amplitude domain". Such a process
can facilitate analysis, for example, where an analyst seeks to
identify one or more features. Where the second derivative values
are rendered with respect to time or depth and position, the result
may be considered an "image" and the process an example of seismic
image enhancement.
[0090] As an example, a method can include accessing seismic data;
providing a wave function that defines, at least in part, a
correlation window length; generating local autocorrelation
functions for the seismic data using the correlation window length;
performing cross-correlations between the wave function and each of
the local autocorrelation functions to provide local
cross-correlation coefficient values; determining second
derivatives of the local cross-correlation coefficient values to
provide local second derivative values; and rendering the local
second derivative values to a display. In such an example, the
accessing seismic data may include accessing seismic data as
amplitude versus time or depth and a spatial dimension. As an
example, a method can include rendering local second derivative
values to a display as local second derivative values versus time
or depth and a spatial dimension. As an example, a method can
include picking one or more horizons based on rendering of local
second derivative values to a display.
[0091] As an example, where a method includes providing a wave
function, such a wave function may be a cosine function for a
single frequency (e.g., or a sine function shifted in phase). As an
example, a method may include repeating where, for each repetition
of the method, the method includes providing a wave function for a
different single frequency (e.g., cosine functions, each of a
different frequency). As an example, a method can include rendering
a graphical user interface to a display where the graphical user
interface includes a graphical control for input of a frequency for
a wave function.
[0092] As an example, a method can include rendering a graphical
user interface to a display where the graphical user interface
includes a graphical control for selection of an attribute that
effectuates at least performing cross-correlations between
autocorrelation functions of seismic data and a wave function
(e.g., specified at a single frequency). As an example, a method
may include rendering a graphical user interface to a display where
the graphical user interface includes a graphical control for
selection of an attribute that effectuates at least such performing
cross-correlations as well as determining second derivatives of
cross-correlation coefficient values.
[0093] As an example, one or more computer-readable media can
include computer-executable instructions to instruct a computing
system to: access seismic data from a storage device (e.g.,
optionally via a network); receive at least one parameter to define
a wave function that determines, at least in part, a correlation
window length; generate local autocorrelation functions for the
seismic data using the correlation window length; perform
cross-correlations between the wave function and each of the local
autocorrelation functions to provide local cross-correlation
coefficient values; determine second derivatives of the local
cross-correlation coefficient values to provide local second
derivative values; and store the local second derivative values to
a storage device. In such an example, computer-executable
instructions may be provided to instruct a computer system to
render a graphical user interface to a display for display of a
selectable attribute to instruct the computer system to execute the
instructions to perform cross-correlations and to execute the
instructions to determine second derivatives.
[0094] As an example, one or more computer-readable media may
include computer-executable instructions to instruct a computer
system to receive at least one parameter such as a frequency for a
wave function, which may be a cosine function. As an example, one
or more computer-readable media may include computer-executable
instructions to instruct a computer system to render local second
derivative values to a display. In such an example, the
computer-executable instructions may provide for rendering the
second derivative values to the display using a color scheme.
[0095] As an example, a system can include one or more processors;
memory; a network interface; a display interface; and
processor-executable instructions stored in the memory to receive
seismic data via the network interface, generate local
autocorrelation functions for the seismic data using a correlation
window length, perform cross-correlations between a wave function
and each of the local autocorrelation functions to provide local
cross-correlation coefficient values, determine second derivatives
of the local cross-correlation coefficient values to provide local
second derivative values, and transmit signals via the display
interface to render the local second derivative values to a
display. In such a system, the wave function may be, for example, a
cosine function. As an example, a wave function may be a wave
function characterized by a single frequency.
[0096] As an example, a system may include instructions to receive
or access seismic data as amplitude versus time or depth and a
spatial dimension. As an example, a system may generate signals to
render local second derivative values to a display, for example,
where such signals provide for rendering local second derivative
values versus time or depth and a spatial dimension. As an example,
a system may include processor-executable instructions stored in
memory to pick a horizon responsive to receipt of an input command
during rendering of local second derivative values, of
cross-correlation coefficient values, to a display.
[0097] FIG. 5 shows an example of a method 500. The method 500
includes a transform 550 for transforming functions. The transform
550 may provide for analyzing a resultant set of seismic data
(e.g., generated in response to a seismic operation performed on a
particular portion of a formation). As an example, the transform
550 may provide for determining geologic characteristics of a
particular portion of a formation, which may be represented as
seismic volume data 510.
[0098] In the example of FIG. 5, to analyze the seismic volume data
510 via the transform 550, one or more autocorrelation functions
530 of the seismic volume data 510 may be generated via an
autocorrelation technique 520 and one or more kernel functions 540
may be provided. A kernel function may be a synthetic time series,
for example, that represents a potential geologic feature of
interest. A kernel function may be compared to seismic data (e.g.,
a trace), for example, over a time window, to determine to what
extent the seismic data may be represented by the kernel function
(e.g., to provide information as to a seismic signature within that
time window). Where multiple kernel functions are provided, seismic
data may be analyzed with respect to each of the multiple kernel
function, for example, to determine which kernel function best
represents aspects of the seismic data.
[0099] As an example, the transform 550 may include
cross-correlating functions. For example, the transform 550 may
cross-correlate one or more of the autocorrelation functions 530
and one or more of the kernel functions 540. As an output, the
transform 530 may generate volume data where spatial location
(e.g., geographic positions or position and time/depth) of a trace
of seismic volume data 510 is preserved and another "dimension"
corresponds to sequenced peak correlation values (e.g., for a
cross-correlation transform) for a collection of kernel functions.
In the example of FIG. 5, the output of the transform 550 is shown
as a correlation spectral volume 560 (e.g., an approximate
graphical representation of actual data). A correlation spectral
volume can include sequenced peak correlation values (e.g., in the
case of cross-correlation) for one or more kernel functions.
[0100] In FIG. 5, the seismic volume data 510 represents an initial
3D seismic volume as an input. The processing technique 520
generates one or more autocorrelation functions, for example,
within a specified window, which may be output as the
autocorrelation functions 530 (e.g., a preprocessing operation).
While a time window is mentioned, a process may be applied,
optionally in parallel, to generate an autocorrelation function for
an entire trace; noting that a full trace autocorrelation function
tends to be insensitive to geology and tends to be more
representative of seismic wavelet.
[0101] In the example of FIG. 5, once the autocorrelation functions
530 are generated, the autocorrelation functions 530 may be
transformed via the transform 550, which may apply a
cross-correlation technique using one or more kernel functions. In
such an example, the autocorrelation functions 530 may be deemed to
include "unknown" characteristics while the kernel functions 540
may be deemed to include "known" characteristics. As an example, a
possible kernel function could be derived from dominant spectral
frequencies of a geologic section (e.g., 8 Hz, 35 Hz, and 65
Hz).
[0102] In the example of FIG. 5, the seismic volume data 510 may
represent an input "window of interest" that includes a subset of
some seismic data traces. In the example of FIG. 5, each of the
seismic traces in the seismic volume data 510 may be subject to the
autocorrelation technique 520. By applying the autocorrelation
technique 520 to the seismic volume data 510, autocorrelation
functions 530 may be produced. The one or more kernel functions 540
may include one or more "seismic trace like" functions that, for
example, intend to inherently represent and correspond to one or
more known geologic features of a formation. Given the
autocorrelation functions 530 and the one or more kernel functions
540, in the example of FIG. 5, the transform 550 may act to compare
"unknown" features of the autocorrelation functions 530 and "known"
features of the one or more kernel functions 540. As mentioned,
output of the transform 550 may be in the form of a correlation
spectral volume 560.
[0103] In the example of FIG. 5, where multiple kernel functions
are provided, each of the kernel functions 540 can undergo
cross-correlation with each of the autocorrelation functions 530,
via the transform 550, and, as a result, the correlation spectral
volume 560 may be generated. As an example, a first kernel function
of the kernel functions 540 may be cross-correlated with each of
the autocorrelation functions 530 to thereby a first row of the
correlation spectral volume 560.
[0104] For the seismic volume data 510 of the example of FIG. 5, a
first of the kernel functions 540 is cross-correlated with each of
the autocorrelation functions 530 to generate a row of the
correlation spectral volume 560. Such a process continues until the
last remaining one of the kernel functions 540 is cross-correlated
with each of the autocorrelation functions 530 to generate the last
remaining row of the correlation spectral volume 560. In such an
example, if a particular cross-correlation coefficient value in the
correlation spectral volume 560 is a high value, this indicates
that the geologic characteristic associated with one particular
kernel function substantially matches the geologic characteristic
associated with one particular autocorrelation function; and, since
the geologic characteristic of the one particular kernel function
is a known quantity, then, the unknown geologic characteristic of
the one particular autocorrelation function could be interpreted as
substantially equal to the known geologic characteristic of the one
particular kernel function. As a result, the "unknown" geologic
characteristics of one or more of the autocorrelation functions 530
and therefore one or more of the input seismic volume data traces
510 can be determined from the "known" geologic characteristics of
one or more of the kernel functions 540 by viewing the values of
the cross-correlation coefficients in the correlation spectral
volume 560.
[0105] The method 500 of FIG. 5 may provide a horizontal slice
through the seismic volume data 510, for example, a slice
orthogonal to a time or depth dimension. Such a slice may
facilitate analysis, for example, to identify one or more features
such as a channel in a subterranean formation. The same slice may
be analyzed for two or more frequencies and the results compared.
For example, a slice at 35 Hz may be compared to a slice at 65 Hz
to understand frequency content in the seismic volume data 510.
[0106] In the example of FIG. 4, a method akin to the method 500 of
FIG. 5 may be applied to provide an intermediate result as a slice
in a plane along time or depth and position (e.g., position
orthogonal to a time or depth axis). A second derivative operation
may be applied to such an intermediate result to provide a final
result that may facilitate picking one or more stratigraphic
features.
[0107] FIG. 6 shows an example of a graphical user interface (GUI)
600 that includes various graphical controls, fields, etc. In the
example of FIG. 6, the GUI 600 pertains to attributes, for example,
of an attribute library. A graphical control of the GUI 600 allows
a user to input a command to select one of a variety of attributes.
For example, a user may navigate an input device to select the
"Iso-frequency & 2nd Derivative" attribute. As indicated in an
information field, this attribute provides, for a selected
frequency, calculation of second derivatives of values of
cross-correlation coefficient as a seismic image enhancement
technique. As indicated, it can determine correlation window length
as cycle length divided by frequency, which is illustrated in a
plot 610. As to input of seismic data, a graphical control 622
allows a user to select seismic data. As to output of processed
data, a graphical control 624 allows a user to optionally specify
how to output processed seismic data. As to input of a frequency, a
graphical control 632 allows a user to slide or type a frequency
and another graphical control 634 allows a user to slide or type a
cycle length. The GUI 600 may include a spectral normalization
graphical control 642 and may include a graphical control 644 for
selecting a discretization technique for purposes of performing a
second derivative operation (e.g., according to one or more
discrete approximations to a second derivative).
[0108] FIG. 7 shows an example of a discretized first derivative
710 and an example of a discretized second derivative 720; noting
that a second derivative may be effectuated by two applications of
a first derivative. As to the second derivative 720, an example of
a matrix that include values of cross-correlation coefficient
versus time or depth and a spatial dimension. As an example, a
discretized second derivative is applied to a particular entry in
the matrix.
[0109] FIG. 8 shows examples of values of cross-correlation
coefficient versus time or depth and a spatial dimension for
various different selected frequencies F.sub.1 to F.sub.8 (e.g.,
for a cosine function), which, in turn, correspond to different
correlation window lengths (e.g., which may be given in units of
time). For the examples of FIG. 8, as frequency decreases from
F.sub.1 to F.sub.8, various features become more prominent. The
examples of FIG. 8 illustrate how features may be associated with a
cosine function for a given frequency.
[0110] FIG. 9 shows an example plot 900 of seismic data plotted
with respect to time or depth and a spatial dimension. The plot 900
is displayed in color to convey amplitude information, for example,
where red is maximum amplitude and blue is minimum amplitude.
[0111] FIG. 10 shows an example plot 1000 of values of
cross-correlation coefficient for seismic data and a cosine
function, the values plotted with respect to time or depth and a
spatial dimension. The plot 1000 is displayed in color to convey
frequency/cross-correlation information, for example, where red and
yellow correspond to high values of cross-correlation for the
selected frequency of the cosine function (e.g., assuming a cycle
length of 1).
[0112] FIG. 11 shows an example plot 1100 of second derivative
values of cross-correlation coefficient for seismic data and a
cosine function, the second derivative values plotted with respect
to time or depth and a spatial dimension. The plot 1100 is
displayed in color to convey second derivative of
frequency/cross-correlation information. In FIG. 11, the plot 1100
may be considered an enhanced image of the plot 900. Such
enhancement may be achieved by, for example, selecting the
"Iso-frequency & 2nd derivative" attribute in the GUI 600,
performing the method 400 of FIG. 4, or one or more other manners
(e.g., where cross-correlation and second derivative operations are
applied).
[0113] FIG. 12 shows an example of a system 1200 that includes
various management components 1210 to manage various aspects of a
geologic environment 1250 (e.g., an environment that includes a
sedimentary basin). For example, the management components 1210 may
allow for direct or indirect management of sensing, drilling,
injecting, extracting, etc., with respect to the geologic
environment 1250. In turn, further information about the geologic
environment 1250 may become available as feedback 1260 (e.g.,
optionally as input to one or more of the management components
1210).
[0114] In the example of FIG. 12, the management components 1210
include a seismic data component 1212, an additional information
component 1214 (e.g., well/logging data), a processing component
1216, a simulation component 1220, an attribute component 1230, an
analysis/visualization component 1242 and a workflow component
1244. In operation, seismic data and other information provided per
the components 1212 and 1214 may be input to the simulation
component 1220.
[0115] In an example embodiment, the simulation component 1220 may
rely on entities 1222. Entities 1222 may include earth entities or
geological objects such as wells, surfaces, reservoirs, etc. In the
system 1200, the entities 1222 can include virtual representations
of actual physical entities that are reconstructed for purposes of
simulation. The entities 1222 may include entities based on data
acquired via sensing, observation, etc. (e.g., the seismic data
1212 and other information 1214).
[0116] In an example embodiment, the simulation component 1220 may
rely on a software framework such as an object-based framework. In
such a framework, entities may include entities based on
pre-defined classes to facilitate modeling and simulation. A
commercially available example of an object-based framework is the
MICROSOFT.RTM..NET.TM. framework (Redmond, Wash.), which provides a
set of extensible object classes. In the .NET.TM. framework, an
object class encapsulates a module of reusable code and associated
data structures. Object classes can be used to instantiate object
instances for use in by a program, script, etc. For example,
borehole classes may define objects for representing boreholes
based on well data.
[0117] In the example of FIG. 12, the simulation component 1220 may
process information to conform to one or more attributes specified
by the attribute component 1230, which may include a library of
attributes (see, e.g., attributes of the example of FIG. 6). Such
processing may occur prior to input to the simulation component
1220. Alternatively, or in addition, the simulation component 1220
may perform operations on input information based on one or more
attributes specified by the attribute component 1230. In an example
embodiment, the simulation component 1220 may construct one or more
models of the geologic environment 1250, which may be relied on to
simulate behavior of the geologic environment 1250 (e.g.,
responsive to one or more acts, whether natural or artificial). In
the example of FIG. 12, the analysis/visualization component 1242
may allow for interaction with a model or model-based results.
Additionally, or alternatively, output from the simulation
component 1220 may be input to one or more other workflows, as
indicated by a workflow component 1244.
[0118] In an example embodiment, the management components 1210 may
include features of a commercially available simulation framework
such as the PETREL.RTM. seismic to simulation software framework
(Schlumberger Limited, Houston, Tex.). The PETREL.RTM. framework
provides components that allow for optimization of exploration and
development operations. The PETREL.RTM. framework includes seismic
to simulation software components that can output information for
use in increasing reservoir performance, for example, by improving
asset team productivity. Through use of such a framework, various
professionals (e.g., geophysicists, geologists, and reservoir
engineers) can develop collaborative workflows and integrate
operations to streamline processes. Such a framework may be
considered an application and may be considered a data-driven
application (e.g., where data is input for purposes of simulating a
geologic environment).
[0119] In an example embodiment, various aspects of the management
components 1210 may include add-ons or plug-ins that operate
according to specifications of a framework environment. For
example, a commercially available framework environment marketed as
the OCEAN.RTM. framework environment (Schlumberger Limited,
Houston, Tex.) allows for seamless integration of add-ons (or
plug-ins) into a PETREL.RTM. framework workflow. The OCEAN.RTM.
framework environment leverages .NET.RTM. tools (Microsoft
Corporation, Redmond, Wash.) and offers stable, user-friendly
interfaces for efficient development. In an example embodiment,
various components may be implemented as add-ons (or plug-ins) that
conform to and operate according to specifications of a framework
environment (e.g., according to application programming interface
(API) specifications, etc.).
[0120] FIG. 12 also shows an example of a framework 1270 that
includes a model simulation layer 1280 along with a framework
services layer 1290, a framework core layer 1295 and a modules
layer 1275. The framework 1270 may include the commercially
available OCEAN.RTM. framework where the model simulation layer
1280 is the commercially available PETREL.RTM. model-centric
software package that hosts OCEAN.RTM. framework applications. In
an example embodiment, the PETREL.RTM. software may be considered a
data-driven application. The PETREL.RTM. software can include a
framework for model building and visualization.
[0121] The model simulation layer 1280 may provide domain objects
1282, act as a data source 1284, provide for rendering 1286 and
provide for various user interfaces 1288. Rendering 1286 may
provide a graphical environment in which applications can display
their data while the user interfaces 1288 may provide a common look
and feel for application user interface components.
[0122] In the example of FIG. 12, the domain objects 1282 can
include entity objects, property objects and optionally other
objects. Entity objects may be used to geometrically represent
wells, surfaces, reservoirs, etc., while property objects may be
used to provide property values as well as data versions and
display parameters. For example, an entity object may represent a
well where a property object provides log information as well as
version information and display information (e.g., to display the
well as part of a model).
[0123] In the example of FIG. 12, data may be stored in one or more
data sources (or data stores, generally physical data storage
devices), which may be at the same or different physical sites and
accessible via one or more networks. The model simulation layer
1280 may be configured to model projects. As such, a particular
project may be stored where stored project information may include
inputs, models, results and cases. Thus, upon completion of a
modeling session, a user may store a project. At a later time, the
project can be accessed and restored using the model simulation
layer 1280, which can recreate instances of the relevant domain
objects.
[0124] In the example of FIG. 12, the geologic environment 1250 may
be outfitted with any of a variety of sensors, detectors,
actuators, etc. For example, equipment 1252 may include
communication circuitry to receive and to transmit information with
respect to one or more networks 1255. Such information may include
information associated with downhole equipment 1254, which may be
equipment to acquire information, to assist with resource recovery,
etc. Other equipment 1256 may be located remote from a well site
and include sensing, detecting, emitting or other circuitry. Such
equipment may include storage and communication circuitry to store
and to communicate data, instructions, etc.
[0125] As an example, various aspects of the management components
1210 may be implemented as add-ons or plug-ins that operate
according to specifications of a framework environment. For
example, a commercially available framework environment marketed as
the OCEAN.RTM. framework environment (Schlumberger Limited) allows
for seamless integration of add-ons (or plug-ins) into a
PETREL.RTM. framework workflow. The OCEAN.RTM. framework
environment leverages .NET.RTM. tools (Microsoft Corporation,
Redmond, Wash.) and offers stable, user-friendly interfaces for
efficient development. As described herein, various components may
be implemented as add-ons (or plug-ins) that conform to and operate
according to specifications of a framework environment (e.g.,
according to application programming interface (API)
specifications, etc.). Various technologies described herein may be
optionally implemented as components in an attribute library (see,
e.g., the attribute component 1230).
[0126] In the field of seismic analysis, aspects of a geologic
environment may be defined as attributes. As an example, seismic
attributes can help to condition amplitude seismic data for
improved structural interpretation tasks, such as determining the
exact location of lithological terminations and helping isolate
hidden seismic stratigraphic features of a geologic environment.
Attribute analysis can be quite helpful to defining a trap in
exploration or delineating and characterizing a reservoir at the
appraisal and development phase. An attribute generation process
(e.g., in the PETREL.RTM. framework or other framework) may rely on
a library of various seismic attributes (e.g., for display and use
with seismic interpretation and reservoir characterization
workflows). As an example, generation of attributes may occur on
the fly for rapid analysis while, as another example, attribute
generation may occur as a background process (e.g., a lower
priority thread in a multithreaded computing environment), which
can allow for one or more foreground processes (e.g., to enable a
user to continue using various components).
[0127] Attributes can help extract value from seismic and other
data, for example, by providing more detail on subtle lithological
variations of a geologic environment (e.g., an environment that
includes one or more reservoirs).
[0128] As described herein, one or more computer-readable media may
include computer-executable instructions to instruct a computing
system to output information for controlling a process. For
example, such instructions may provide for output to sensing
process, an injection process, drilling process, an extraction
process, etc.
[0129] FIG. 13 shows components of an example of a computing system
1300 and an example of a networked system 1310. The system 1300
includes one or more processors 1302, memory and/or storage
components 1304, one or more input and/or output devices 1306 and a
bus 1308. In an example embodiment, instructions may be stored in
one or more computer-readable media (e.g., memory/storage
components 1304). Such instructions may be read by one or more
processors (e.g., the processor(s) 1302) via a communication bus
(e.g., the bus 1308), which may be wired or wireless. The one or
more processors may execute such instructions to implement (wholly
or in part) one or more attributes (e.g., as part of a method). A
user may view output from and interact with a process via an I/O
device (e.g., the device 1306). In an example embodiment, a
computer-readable medium may be a storage component such as a
physical memory storage device, for example, a chip, a chip on a
package, a memory card, etc.
[0130] In an example embodiment, components may be distributed,
such as in the network system 1310. The network system 1310
includes components 1322-1, 1322-2, 1322-3, . . . 1322-N. For
example, the components 1322-1 may include the processor(s) 1302
while the component(s) 1322-3 may include memory accessible by the
processor(s) 1302. Further, the component(s) 1302-2 may include an
I/O device for display and optionally interaction with a method.
The network may be or include the Internet, an intranet, a cellular
network, a satellite network, etc.
[0131] Although only a few example embodiments have been described
in detail above, those skilled in the art will readily appreciate
that many modifications are possible in the example embodiments
without materially departing from a radial bearing assembly (or
assemblies) for a centrifugal pump. Accordingly, all such
modifications are intended to be included within the scope of this
disclosure as defined in the following claims. In the claims,
means-plus-function clauses are intended to cover the structures
described herein as performing the recited function and not only
structural equivalents, but also equivalent structures. Thus,
although a nail and a screw may not be structural equivalents in
that a nail employs a cylindrical surface to secure wooden parts
together, whereas a screw employs a helical surface, in the
environment of fastening wooden parts, a nail and a screw may be
equivalent structures. It is the express intention of the applicant
not to invoke 35 U.S.C. .sctn.112, paragraph 6 for any limitations
of any of the claims herein, except for those in which the claim
expressly uses the words "means for" together with an associated
function.
* * * * *